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We propose an approach to 4D print solvent-triggered, gradient-based bilayers made of semi-crystalline crosslinked polymer networks. Out-of-plane bending is obtained after immersion in the solvent, exploiting the different swelling degrees of the layers resulting from crosslinking gradients. Lastly, a beam model of the shape transformation is applied and experimentally validated., Comment: This work was funded by the European Union ERC CoDe4Bio Grant ID 101039467 under the funding programme Horizon Europe

Introduction: Circadian rhythm disruption has garnered significant attention for its adverse effects on human health, particularly in reproductive medicine and fetal well-being. Assessing pregnancy health often relies on diagnostic markers such as the labyrinth zone (LZ) proportion within the placenta. This study aimed to investigate the impact of disrupted circadian rhythms on placental health and fetal development using animal models. Methods and Results: Employing unstained photo-acoustic microscopy (PAM) and hematoxylin and eosin (HE)-stained images, we found them mutually reinforcing. Our images revealed the role of MCRD on the LZ and fetus weight: a decrease in LZ area from 5.01-HE(4.25-PAM) mm2 to 3.58-HE (2.62-PAM) mm2 on day 16 and 6.48-HE(5.16-PAM) mm2 to 4.61-HE (3.03-PAM) mm2 on day 18, resulting in 0.71 times lower fetus weights. We have discriminated a decrease in the mean LZ to placenta area ratio from 64% to 47% on day 18 in mice with disrupted circadian rhythms with PAM. Discussion: The study highlights the negative influence of circadian rhythm disruption on placental development and fetal well-being. Reduced LZ area and fetal weights in the MCRD group suggest compromised placental function under disrupted circadian rhythms. PAM imaging proved to be an efficient technique for assessing placental development, offering advantages over traditional staining methods. These findings contribute to understanding the mechanisms underlying circadian disruption's effects on reproductive health and fetal development, emphasizing the importance of maintaining normal circadian rhythms for optimal pregnancy outcomes. Further research is needed to explore interventions to mitigate these effects and improve pregnancy outcomes., Comment: 9 pages, 3 figures

Impact assessments have emerged as a common way to identify the negative and positive implications of AI deployment, with the goal of avoiding the downsides of its use. It is undeniable that impact assessments are important - especially in the case of rapidly proliferating technologies such as generative AI. But it is also essential to critically interrogate the current literature and practice on impact assessment, to identify its shortcomings, and to develop new approaches that are responsive to these limitations. In this provocation, we do just that by first critiquing the current impact assessment literature and then proposing a novel approach that addresses our concerns: Scenario-Based Sociotechnical Envisioning., Comment: Publication at NeurIPS'24 workshop Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI (EvalEval)

The connection between galaxies and dark matter halos encompasses a range of processes and play a pivotal role in our understanding of galaxy formation and evolution. Traditionally, this link has been established through physical or empirical models. Machine learning techniques are adaptable tools that handle high-dimensional data and grasp associations between numerous attributes. In particular, probabilistic models capture the stochasticity inherent to these complex relations. We compare different probabilistic machine learning methods to model the uncertainty in the halo-galaxy connection and efficiently generate galaxy catalogs that faithfully resemble the reference sample by predicting joint distributions of central galaxy properties conditioned to their host halo features. The analysis is based on the IllustrisTNG300 simulation. The methods model the distributions in different ways. We compare a multilayer perceptron that predicts the parameters of a multivariate Gaussian distribution, a multilayer perceptron classifier, and the method of normalizing flows. The classifier predicts the parameters of a Categorical distribution, which are defined in a high-dimensional parameter space through a Voronoi cell-based hierarchical scheme. We evaluate the model's performances under various sample selections based on halo properties. The three methods exhibit comparable results, with normalizing flows showing the best performance in most scenarios. The models reproduce the main features of galaxy properties distributions with high-fidelity and reproduce the results obtained with traditional, deterministic, estimators. Our results also indicate that different halos and galaxy populations are subject to varying degrees of stochasticity, which has relevant implications for studies of large-scale structure., Comment: 14 pages, 8 figures, 6 tables

The effective adoption of responsible AI practices in journalism requires a concerted effort to bridge different perspectives, including technological, editorial, journalistic, and managerial. Among the many challenges that could impact information sharing around responsible AI inside news organizations are knowledge silos, where information is isolated within one part of the organization and not easily shared with others. This study aims to explore if, and if so, how, knowledge silos affect the adoption of responsible AI practices in journalism through a cross-case study of four major Dutch media outlets. We examine the individual and organizational barriers to AI knowledge sharing and the extent to which knowledge silos could impede the operationalization of responsible AI initiatives inside newsrooms. To address this question, we conducted 14 semi-structured interviews with editors, managers, and journalists at de Telegraaf, de Volkskrant, the Nederlandse Omroep Stichting (NOS), and RTL Nederland. The interviews aimed to uncover insights into the existence of knowledge silos, their effects on responsible AI practice adoption, and the organizational practices influencing these dynamics. Our results emphasize the importance of creating better structures for sharing information on AI across all layers of news organizations.

Structural identifiability is an important property of parametric ODE models. When conducting an experiment and inferring the parameter value from the time-series data, we want to know if the value is globally, locally, or non-identifiable. Global identifiability of the parameter indicates that there exists only one possible solution to the inference problem, local identifiability suggests that there could be several (but finitely many) possibilities, while non-identifiability implies that there are infinitely many possibilities for the value. Having this information is useful since, one would, for example, only perform inferences for the parameters which are identifiable. Given the current significance and widespread research conducted in this area, we decided to create a database of linear compartment models and their identifiability results. This facilitates the process of checking theorems and conjectures and drawing conclusions on identifiability. By only storing models up to symmetries and isomorphisms, we optimize memory efficiency and reduce query time. We conclude by applying our database to real problems. We tested a conjecture about deleting one leak of the model states in the paper 'Linear compartmental models: Input-output equations and operations that preserve identifiability' by E. Gross et al., and managed to produce a counterexample. We also compute some interesting statistics related to the identifiability of linear compartment model parameters.

In this paper, we consider the existence and spectral stability of periodic traveling wave solutions for the regularized Camassa-Holm equation. For the existence of periodic waves, we employ tools from bifurcation theory to construct waves with the zero mean property and prove that waves with the same property may not exist for the well known Camassa-Holm equation. Regarding the spectral stability, we analyze the difference between the number of negative eigenvalues of a convenient linear operator, restricted to the space constituted by zero-mean periodic functions, and the number of negative eigenvalues of the matrix formed by the tangent space associated with the low-order conserved quantities of the evolution model. We also discuss the problem of orbital stability in the energy space., Comment: 15 pages

In this paper, we present new results regarding the orbital stability of solitary standing waves for the general fourth-order Schr\"odinger equation with mixed dispersion. The existence of solitary waves can be determined both as minimizers of a constrained complex functional and by using a numerical approach. In addition, for specific values of the frequency associated with the standing wave, one obtains explicit solutions with a hyperbolic secant profile. Despite these explicit solutions being minimizers of the constrained functional, they cannot be seen as a smooth curve of solitary waves, and this fact prevents their determination of stability using classical approaches in the current literature. To overcome this difficulty, we employ a numerical approach to construct a smooth curve of solitary waves. The existence of a smooth curve is useful for showing the existence of a threshold power $\alpha_0\approx 4.8$ of the nonlinear term such that if $\alpha\in (0,\alpha_0),$ the explicit solitary wave is stable, and if $\alpha>\alpha_0$, the wave is unstable. An important feature of our work, caused by the presence of the mixed dispersion term, concerns the fact that the threshold value $\alpha_0 \approx 4.8$ is not the same as that established for proving the existence of global solutions in the energy space, as is well known for the classical nonlinear Schr\"odinger equation., Comment: 20 pages and 16 figures

Advances in Generative Artificial Intelligence (AI) are resulting in AI-generated media output that is (nearly) indistinguishable from human-created content. This can drastically impact users and the media sector, especially given global risks of misinformation. While the currently discussed European AI Act aims at addressing these risks through Article 52's AI transparency obligations, its interpretation and implications remain unclear. In this early work, we adopt a participatory AI approach to derive key questions based on Article 52's disclosure obligations. We ran two workshops with researchers, designers, and engineers across disciplines (N=16), where participants deconstructed Article 52's relevant clauses using the 5W1H framework. We contribute a set of 149 questions clustered into five themes and 18 sub-themes. We believe these can not only help inform future legal developments and interpretations of Article 52, but also provide a starting point for Human-Computer Interaction research to (re-)examine disclosure transparency from a human-centered AI lens., Comment: Accepted to CHI 2024 Late-Breaking Work

We propose to extend coherent laser cooling from narrow-band to broad-band transitions by using trains of ultrashort broadband pulses. We study analytically two possible methods to reduce the momentum spread of a distribution by several units of photon momentum in a single spontaneous emission lifetime. We report on numerical simulations of one-dimensional laser cooling of a two-level system with realistic parameters. The technique introduced here is of high interest for efficient laser cooling of fast species with short lifetime such as positronium.

As a general purpose technology without a concrete pre-defined purpose, personal chatbots can be used for a whole range of objectives, depending on the personal needs, contexts, and tasks of an individual, and so potentially impact a variety of values, people, and social contexts. Traditional methods of risk assessment are confronted with several challenges: the lack of a clearly defined technology purpose, the lack of clearly defined values to orient on, the heterogeneity of uses, and the difficulty of actively engaging citizens themselves in anticipating impacts from the perspective of their individual lived realities. In this article, we leverage scenario writing at scale as a method for anticipating AI impact that is responsive to these challenges. The advantages of the scenario method are its ability to engage individual users and stimulate them to consider how chatbots are likely to affect their reality and so collect different impact scenarios depending on the cultural and societal embedding of a heterogeneous citizenship. Empirically, we tasked 106 US-based participants to write short fictional stories about the future impact (whether desirable or undesirable) of AI-based personal chatbots on individuals and society and, in addition, ask respondents to explain why these impacts are important and how they relate to their values. In the analysis process, we map those impacts and analyze them in relation to socio-demographic as well as AI-related attitudes of the scenario writers. We show that our method is effective in (1) identifying and mapping desirable and undesirable impacts of AI-based personal chatbots, (2) setting these impacts in relation to values that are important for individuals, and (3) detecting socio-demographic and AI-attitude related differences of impact anticipation.

In this paper, we present a novel convolution theorem which encompasses the well known convolution theorem in (graph) signal processing as well as the one related to time-varying filters. Specifically, we show how a node-wise convolution for signals supported on a graph can be expressed as another node-wise convolution in a frequency domain graph, different from the original graph. This is achieved through a parameterization of the filter coefficients following a basis expansion model. After showing how the presented theorem is consistent with the already existing body of literature, we discuss its implications in terms of non-stationarity. Finally, we propose a data-driven algorithm based on subspace fitting to learn the frequency domain graph, which is then corroborated by experimental results on synthetic and real data., Comment: 13 Pages; Submitted for possible publication